Reduction of the scanning time by total variation minimization reconstruction for X-ray tomography in a SEM

2014 ◽  
Vol 256 (2) ◽  
pp. 90-99 ◽  
Author(s):  
D. LALOUM ◽  
T. PRINTEMPS ◽  
D.F. SANCHEZ ◽  
F. LORUT ◽  
G. AUDOIT ◽  
...  
Keyword(s):  
Total Variation ◽  
Total Variation Minimization ◽  
X Ray ◽  
Scanning Time

scholarly journals NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Yan ◽  
Zhao Jin ◽  
Hanming Zhang ◽  
Lei Li ◽  
Ailong Cai
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Imaging System ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Time Duration ◽  
Total Variation Minimization ◽  
Scanning Method ◽  
X Ray ◽  
Alternating Direction

Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT). Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV) regularization for sparse-view CT. Combined with the alternating direction method, the proposed approach shows excellent efficiency and rapid convergence property. Numerical simulations and real data experiments are performed on a parallel beam CT. Experimental results validate that the proposed method has higher computational efficiency and better reconstruction quality than the conventional algorithms, such as simultaneous algebraic reconstruction technique using TV method and the alternating direction total variation minimization approach, with the same time duration. The proposed method appears to have extensive applications in X-ray CT imaging.

scholarly journals A constrained, total-variation minimization algorithm for low-intensity x-ray CT

2011 ◽  
Vol 38 (S1) ◽  
pp. S117-S125 ◽  
Author(s):  
Emil Y. Sidky ◽  
Yuval Duchin ◽  
Xiaochuan Pan ◽  
Christer Ullberg
Keyword(s):  
Total Variation ◽  
Minimization Algorithm ◽  
Total Variation Minimization ◽  
X Ray ◽  
Low Intensity

scholarly journals Total variation minimization approach in in-line x-ray phase-contrast tomography

2013 ◽  
Vol 21 (10) ◽  
pp. 12185 ◽  
Author(s):  
Alexander Kostenko ◽  
K. Joost Batenburg ◽  
Andrew King ◽  
S. Erik Offerman ◽  
Lucas J. van Vliet
Keyword(s):  
Total Variation ◽  
Phase Contrast ◽  
Total Variation Minimization ◽  
X Ray ◽  
Phase Contrast Tomography ◽  
Minimization Approach

scholarly journals Phase retrieval in in-line x-ray phase contrast imaging based on total variation minimization

2013 ◽  
Vol 21 (1) ◽  
pp. 710 ◽  
Author(s):  
Alexander Kostenko ◽  
K. Joost Batenburg ◽  
Heikki Suhonen ◽  
S. Erik Offerman ◽  
Lucas J. van Vliet
Keyword(s):  
Total Variation ◽  
Phase Contrast ◽  
Phase Retrieval ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
Total Variation Minimization ◽  
X Ray

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

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